CN108871427B - Water quality detection method for water source area - Google Patents
Water quality detection method for water source area Download PDFInfo
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- CN108871427B CN108871427B CN201810428956.XA CN201810428956A CN108871427B CN 108871427 B CN108871427 B CN 108871427B CN 201810428956 A CN201810428956 A CN 201810428956A CN 108871427 B CN108871427 B CN 108871427B
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- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 title claims abstract description 108
- 238000001514 detection method Methods 0.000 title claims abstract description 33
- 230000004927 fusion Effects 0.000 claims abstract description 29
- 239000003673 groundwater Substances 0.000 claims abstract description 4
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 claims description 14
- 239000011159 matrix material Substances 0.000 claims description 14
- 238000000034 method Methods 0.000 claims description 13
- 239000003651 drinking water Substances 0.000 claims description 9
- 235000020188 drinking water Nutrition 0.000 claims description 9
- 238000012806 monitoring device Methods 0.000 claims description 5
- 229910052799 carbon Inorganic materials 0.000 claims description 4
- 230000001360 synchronised effect Effects 0.000 claims description 4
- 238000005259 measurement Methods 0.000 claims description 2
- 238000012544 monitoring process Methods 0.000 description 9
- 238000010586 diagram Methods 0.000 description 4
- 230000005540 biological transmission Effects 0.000 description 3
- 239000007787 solid Substances 0.000 description 3
- 230000007613 environmental effect Effects 0.000 description 1
- 238000000746 purification Methods 0.000 description 1
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- G—PHYSICS
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- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
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- G01D21/02—Measuring two or more variables by means not covered by a single other subclass
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- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
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Abstract
The invention provides a water quality detection method for a water source area, which comprises the following steps: distributing a plurality of turbidity sensors under water in a water source area, and converting numerical values measured by the turbidity sensors into turbidity matrixes; calculating a grey correlation degree fusion weight of the water source environment turbidity sensor value based on the grey correlation degree; calculating a similarity fusion weight of the water source environment turbidity sensor values based on the similarity; calculating the weight of the combination based on the minimum relative information entropy principle; and obtaining a water source ground water quality fusion model according to the combined weight.
Description
Technical Field
The invention relates to a water quality monitoring method for a water source area, in particular to a water quality monitoring method for a water source area.
Background
The drinking water source environment parameter intelligent monitoring system in the market of China is low in intelligent degree and scientific and technical level, the same water source place can not be monitored at multiple points, the turbidity of the drinking water source place can not be accurately controlled, aiming at the current situation, a drinking water source monitoring device is designed to be capable of carrying out multi-point monitoring on various parameters of a drinking water source place water environment factor, and a water source environment multi-point turbidity fusion model is utilized to accurately fuse turbidity values detected at multiple points of the water source environment, so that the water source environment turbidity detection accuracy, robustness and reliability are improved, and the drinking water source environment turbidity monitoring device has high universality and practicability.
Disclosure of Invention
The invention provides a water quality detection method for a water source, which has high parameter precision for monitoring water factors of a water environment.
The technical scheme for realizing the purpose of the invention is as follows: a water quality detection method for a water source area is characterized by comprising the following steps:
step 2, calculating a grey correlation degree fusion weight of the water source environment turbidity sensor value based on the grey correlation degree;
step 3, calculating a similarity fusion weight of the water source environment turbidity sensor value based on the similarity;
and 5, obtaining a water source ground water quality fusion model according to the combined weight.
Compared with the prior art, the invention has the following advantages: (1) solution (II)The problem that the existing monitoring equipment cannot monitor multiple points of the same water source and accurately control the turbidity of the drinking water source is solved, the turbidity values of the water source environment detected at the multiple points are accurately fused, the detection accuracy, robustness and reliability of the turbidity of the water source environment are improved, the universality and practicability are higher, the monitoring cost is better reduced due to the high intelligent and humanized detection control and regulation capacity, and the monitoring equipment is convenient to use, safe and reliable; (2) converting turbidity parameters of water factors in a water source environment into an interval number form, defining the similarity of every two interval numbers, constructing a similarity matrix, detecting the turbidity in the water source environment once when a mobile trolley provided with turbidity sensors moves 30 degrees, and fusing the similarity of the turbidity sensor values of each detection point into a similarity fusion weight alpha according to the ratio of the similarity of the interval numbers of the turbidity sensors of each detection point to the sum of the interval number similarities of the turbidity sensors of the whole water environmentiThe accuracy and the scientificity of the turbidity fusion value of the water source environment are improved.
The invention is further described below with reference to the accompanying drawings.
Drawings
FIG. 1 is a schematic flow diagram of the process of the present invention.
Fig. 2 is a schematic structural diagram of a lifting track device in a drinking water source monitoring device.
Fig. 3 is a schematic structural diagram of an activated carbon dosing device according to the present invention.
Fig. 4 is a schematic view of the structure of the circular track of the present invention.
FIG. 5 is a view showing the inner structure of the base in the present invention.
Fig. 6 is a diagram of the screw and rod position distribution in the present invention.
Fig. 7 is a schematic view of a base in the present invention.
Fig. 8 is a partially enlarged view of the traveling carriage according to the present invention.
Detailed Description
With reference to fig. 1, a method for detecting water quality of a water source area is characterized by comprising the following steps:
step 2, calculating a grey correlation degree fusion weight of the water source environment turbidity sensor value based on the grey correlation degree;
step 3, calculating a similarity fusion weight of the water source environment turbidity sensor value based on the similarity;
and 5, obtaining a water source ground water quality fusion model according to the combined weight.
The turbidity matrix in step 1 is
Where m is the number of sensors and n is the number of sensor measurement periods.
The specific process of the step 2 is as follows:
step 2.1, calculating the association degree zeta of each sensor in the K time period and the maximum temperature values of the m sensors in each K time period according to the formula (2)ijK is 1,2, … n,
wherein,
step 2.2, a grey correlation matrix B of the turbidity sensor of the water source environment is constructed,
step 2.3, calculating the average association zeta of the turbidity value detected by each sensor and the maximum turbidity value according to the formula (4)i
Step 2.4, calculating the number of transmissions of each sensor in the k time period and m sensors according to the formula (5)Degree of correlation lambda of minimum turbidity value of sensor in each K periodij
Step 2.5, constructing a correlation matrix C
Step 2.6, calculating the average correlation eta of the turbidity value detected by each sensor and the minimum turbidity value according to the formula (7)i
Step 2.7, solving the fusion weight beta of the grey correlation degree of the water source environment turbidity sensor value according to the formula (8)i
The specific process of the step 3 is as follows:
step 3.1, constructing a similarity matrix S for detecting the turbidity of the water source by the sensors according to the similarity of the turbidity of the water source environment detected by any two different sensors at the same time period
Wherein,
Sabdenotes the degree of similarity between a and b, a ═ aL,aU]，b＝[bL,bU]，qjJ is 1,2,3,4 is aL、aU、bL、bUThe j-th largest number in (a),
step 3.2, calculating momentsAverage similarity S of each sensor of each row of array Si
Step 3.3, calculating the similarity fusion weight alpha of the water source environment turbidity sensor valuei
Weight w of combination based on minimum relative information entropy principle in step 4i
The model in step 5 is
Where i is the index value of the detection point, xiThe ith detection point temperature at time k.
With reference to fig. 2 to 8, the drinking water source monitoring device for implementing the detection method includes an activated carbon feeding device 2, a lifting rail device 1, a water source detection device 21 and a water source environment multi-point turbidity fusion model.
Referring to fig. 3 to 6, the lifting rail device includes a base 7, a lower plate 8, a circular rail 5, and a lifting device connecting the base 7 and the circular rail 5. The lifting device comprises a stepping motor 19, two lead screws 18, a polish rod 17, three belt wheels 15, a synchronous belt 16, two supporting feet 6 with internal threads and a supporting foot 9 with a polish hole. Two supporting legs 6 with internal threads and a supporting leg 9 with a unthreaded hole are distributed at the bottom surface of the annular track 5 at 120 degrees, two lead screws 18 and a polished rod 17 are respectively matched with the two supporting legs 6 with the internal threads and the supporting leg 9 with the unthreaded hole, the lower ends of the two lead screws 18 and the polished rod 17 penetrate through the upper surface of the base 7 and are respectively fixedly connected with three belt wheels 15, the lower ends of the two lead screws 18 are arranged on the lower bottom plate 8 through bearings, the lower end of the polished rod 17 is connected with a driving shaft of a stepping motor 19 arranged on the lower bottom plate 8, and the three belt wheels 15 are connected through a synchronous belt 16.
Referring to fig. 2 and 7, the activated carbon delivery device 2 includes a moving trolley 22, a housing body, a slider-crank mechanism, and a rectangular solid without a cover 13. Travelling car 22 sets up on circular track 5 and around the motion of circular track 5, and on the shell body set up and travelling car 22, the shell body set up the inner chamber, and it has the input mouth with the inner chamber intercommunication to open on the shell body lateral wall, sets up solenoid valve 4 on the input mouth, and slider-crank mechanism sets up in the inner chamber of shell body, and uncovered cuboid 13 bears the weight of the active carbon, and stretches out or the locking input mouth by slider-crank mechanism drive. The crank-slider mechanism comprises a frame 10, a crank 11, a connecting rod 12 and a slider 23. The tail end of the crank 11 is fixed on the bottom surface of the inner cavity of the shell body, the tail end of the connecting rod 12 is rotatably connected with the front end of the crank 11, the tail end of the rack 10 is slidably connected with the crank 11, the sliding block 23 is rotatably connected with the front end of the connecting rod 12 and fixedly connected with the front end of the rack 10, and the sliding block 23 is fixedly connected with the uncovered cuboid 13.
Specifically, a square hole is formed on the surface of the end of the frame 10, and the crank 11 passes through the square hole.
The water source detection device comprises a turbidity sensor, a temperature sensor, a TDS sensor and a pH value sensor. The turbidity sensors are arranged in four, three of the turbidity sensors are uniformly distributed on the base 7 at A, B, C points in fig. 1, and the other turbidity sensor is arranged on the outer wall of the shell body.
The water source environment multi-point turbidity fusion model converts multi-point turbidity values detected by a water source ground detection device in a water source environment into interval numerical values, defines similarity and grey correlation degree of the interval numerical values of turbidity sensors, constructs a similarity matrix and a grey correlation matrix, the similarity fusion weight of each detection point turbidity sensor interval number in the water source environment is defined, the grey correlation fusion weight of each detection point turbidity sensor interval value and the grey correlation fusion weight of the detection point turbidity sensor value is defined as the root-mean-square root of the sum of the reciprocals of the average correlation degree products of the maximum interval numerical values and the minimum interval numerical values of the water source environment turbidity sensor interval value and the inverse sum of the average correlation degree products of the maximum interval numerical values and the minimum interval numerical values of the water source environment turbidity sensor interval value, the root-square root-mean-square root-mean-square root of the similarity fusion The ratio is the combined weight of the fusion of the turbidity sensor values of the detection points, and the sum of the combined weight products of the turbidity sensor values of the detection points and the respective turbidity sensor values in the water source environment is the value of a turbidity fusion model of a plurality of detection points in the water source environment.
The lifting track device is placed in a water source place, the annular track 5 is driven to move up and down through the spiral transmission mechanism, all sensors distributed in the water source place detection device 5 on the side face of the movable trolley respectively detect water quality parameters, and the collected water quality parameter information is communicated with a user through the wireless module, so that the real-time monitoring of the water quality condition of the water source place by the user is realized. Meanwhile, a turbidity fusion model of a plurality of detection points of the water source environment is utilized, the turbidity similarity of the water source environment is detected by any two different sensors at the same time interval, a similarity matrix S for detecting the turbidity of the water source by the sensors is constructed, the turbidity condition of the water source environment of the whole water source area can be controlled more accurately, if the turbidity of the water source area exceeds a preset value, the electromagnetic valve 4 is opened, the uncovered cuboid 13 filled with the solid activated carbon passes through the action of the crank slider mechanism, and the electromagnetic valve 4 is penetrated to stretch out of the water surface for water quality purification.
The working principle is as follows: the lifting track device is placed in a water source place, the lifting motion of the annular track 5 is controlled through the matching of the spiral transmission mechanism and the polished rod guide rail, each sensor in the water source place detection device distributed on the side surface 25 of the movable trolley respectively detects water quality parameters, along with the lifting motion of the annular track, the water source place detection device can detect the water quality conditions of different depths of the water source place, the movable trolley moves on the annular track and can detect the water quality conditions of multiple points on the same horizontal plane of the water source place, the acquired water quality parameter information is communicated with a user through a wireless module, a turbidity fusion model of multiple detection points of the water source environment is utilized, the similarity of the turbidity of the water source environment is detected by any two different sensors in the same time period, a similarity matrix S of the turbidity of the water source detected by the sensors is constructed, and the turbidity condition of the water, therefore, the situation of the water environmental factors of the water source area is accurately monitored by a user. If the turbidity of the water source exceeds the preset value, the electromagnetic valve is opened, and the uncovered cuboid filled with the solid activated carbon passes through the electromagnetic valve and extends out of the water surface to purify the water quality through the action of the slider-crank mechanism.
Claims (6)
1. A water quality detection method for a water source area is characterized by comprising the following steps:
step 1, distributing a plurality of turbidity sensors under water in a water source area, and converting numerical values measured by the turbidity sensors into turbidity matrixes;
step 2, calculating a grey correlation degree fusion weight of the water source environment turbidity sensor value based on the grey correlation degree;
step 3, calculating a similarity fusion weight of the water source environment turbidity sensor value based on the similarity;
step 4, calculating the weight of the combination based on the minimum relative information entropy principle;
step 5, obtaining a water source ground water quality fusion model according to the combination weight;
the drinking water source monitoring device for realizing the detection method comprises an active carbon feeding device (2), a lifting track device (1) and a water source detection device (21);
the active carbon putting device (2) comprises a mobile trolley (22), a shell body, a slider-crank mechanism and a coverless cuboid (13); the movable trolley (22) is arranged on the annular track (5) and moves around the annular track (5), the shell body is arranged on the movable trolley (22), the shell body is provided with an inner cavity, the side wall of the shell body is provided with a putting-in opening communicated with the inner cavity, the putting-in opening is provided with an electromagnetic valve (4), the slider-crank mechanism is arranged in the inner cavity of the shell body, the uncovered cuboid (13) bears active carbon and is driven by the slider-crank mechanism to extend out or lock the putting-in opening; the crank sliding block mechanism comprises a rack (10), a crank (11), a connecting rod (12) and a sliding block (23); the tail end of a crank (11) is fixed on the bottom surface of an inner cavity of the shell body, the tail end of a connecting rod (12) is rotatably connected with the front end of the crank (11), the tail end of a rack (10) is slidably connected with the crank (11), a sliding block (23) is rotatably connected with the front end of the connecting rod (12) and fixedly connected with the front end of the rack (10), and the sliding block (23) is fixedly connected with a non-cover cuboid (13);
the lifting rail device comprises a base (7), a lower bottom plate (8), an annular rail (5) and a lifting device connected with the base (7) and the annular rail (5); the lifting device comprises a stepping motor (19), two lead screws (18), a polish rod (17), three belt wheels (15), a synchronous belt (16), two supporting legs (6) with internal threads and a supporting leg (9) with a polish hole; two supporting feet (6) with internal threads and one supporting foot (9) with a unthreaded hole are distributed on the lower bottom surface of the annular track (5) at 120 degrees, two lead screws (18) and one polished rod (17) are respectively matched with the two supporting feet (6) with the internal threads and the supporting foot (9) with the unthreaded hole, the lower ends of the two lead screws (18) and the polished rod (17) penetrate through the upper surface of the base (7) and are respectively and fixedly connected with three belt wheels (15), the lower ends of the two lead screws (18) are arranged on the lower bottom plate (8) through bearings, the lower end of the polished rod (17) is connected with a driving shaft of a stepping motor (19) arranged on the lower bottom plate (8), and the three belt wheels (15) are connected through a synchronous belt (;
the water source detection device (21) comprises a turbidity sensor, a temperature sensor, a TDS sensor and a PH value sensor; the turbidity sensors are four in number, three of the turbidity sensors are uniformly distributed on the base (7), and the other turbidity sensor is arranged on the outer wall of the shell body.
2. The method of claim 1, wherein the turbidity matrix in step 1 is
Where m is the number of sensors and n is the number of sensor measurement periods.
3. The method according to claim 2, wherein the specific process of step 2 is as follows:
step 2.1, calculate each sensor in k time period according to equation (2)Degree of association ζ with maximum temperature values of m sensors at each K periodijK is 1,2, … n,
step 2.2, a grey correlation matrix B of the turbidity sensor of the water source environment is constructed,
step 2.3, calculating the average association zeta of the turbidity value detected by each sensor and the maximum turbidity value according to the formula (4)i
Step 2.4, calculating the association degree lambda of each sensor in the K period and the minimum turbidity value of the m sensors in each K period according to the formula (5)ij
Step 2.5, constructing a correlation matrix C
Step 2.6, calculating the average relevance degree eta i of the turbidity value detected by each sensor and the minimum turbidity value according to the formula (7)
Step 2.7, obtaining water source environment turbidity sensing according to formula (8)Gray relevance fusion weight beta of device valuei
4. The method according to claim 3, wherein the specific process of step 3 is as follows:
step 3.1, constructing a similarity matrix S for detecting the turbidity of the water source by the sensors according to the similarity of the turbidity of the water source environment detected by any two different sensors at the same time period
Wherein,
Sabdenotes the degree of similarity between a and b, a ═ aL,aU]，b＝[bL,bU]，qjJ is 1,2,3,4 is aL、aU、bL、bUThe j-th largest number in (a),
step 3.2, calculating the average similarity Si of each sensor of each row of the matrix S
Step 3.3, calculating the similarity fusion weight alpha i of the water source environment turbidity sensor value
5. The method according to claim 4, wherein the step 4 is based on the principle of minimum relative entropyWeight w of the combination ofi
6. The method of claim 5, wherein the model in step 5 is
Where i is the index value of the detection point, xiThe ith detection point temperature at time k.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN102053139A (en) * | 2009-10-27 | 2011-05-11 | 中国科学院苏州纳米技术与纳米仿生研究所 | Real-time multiparameter remote water quality monitoring system and method |
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| CN107168403A (en) * | 2017-05-12 | 2017-09-15 | 淮阴工学院 | A kind of environment of chicken house system for detecting temperature based on CAN fieldbus |
| CN107168402A (en) * | 2017-05-12 | 2017-09-15 | 淮阴工学院 | Environment of chicken house temperature intelligent monitoring system based on CAN fieldbus |
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Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102053139A (en) * | 2009-10-27 | 2011-05-11 | 中国科学院苏州纳米技术与纳米仿生研究所 | Real-time multiparameter remote water quality monitoring system and method |
| CN103728428A (en) * | 2013-12-24 | 2014-04-16 | 蒙宏铅 | Water quality real-time online monitoring system |
| CN104077601A (en) * | 2014-07-08 | 2014-10-01 | 中国航空无线电电子研究所 | Method for carrying out synthetic target recognition through information of different types |
| CN107168403A (en) * | 2017-05-12 | 2017-09-15 | 淮阴工学院 | A kind of environment of chicken house system for detecting temperature based on CAN fieldbus |
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